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- NumPy - Installing NumPy
The only prerequisite for installing NumPy is Python itself If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science
- NumPy quickstart — NumPy v2. 3 Manual
NumPy’s main object is the homogeneous multidimensional array It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers
- NumPy Documentation
NumPy 1 20 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1 19 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1 18 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1 17 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1 16 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy
- NumPy user guide — NumPy v2. 3 Manual
NumPy user guide # This guide is an overview and explains the important features; details are found in NumPy reference
- numpy. where — NumPy v2. 3 Manual
numpy where # numpy where(condition, [x, y, ] ) # Return elements chosen from x or y depending on condition
- NumPy fundamentals — NumPy v2. 3 Manual
These documents clarify concepts, design decisions, and technical constraints in NumPy This is a great place to understand the fundamental NumPy ideas and philosophy
- Indexing on ndarrays — NumPy v2. 3 Manual
The native NumPy indexing type is intp and may differ from the default integer array type intp is the smallest data type sufficient to safely index any array; for advanced indexing it may be faster than other types
- Data types — NumPy v2. 3 Manual
NumPy numerical types are instances of numpy dtype (data-type) objects, each having unique characteristics Once you have imported NumPy using import numpy as np you can create arrays with a specified dtype using the scalar types in the numpy top-level API, e g numpy bool, numpy float32, etc
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